


import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = True		,							SCREAMING_SNAKE_CASE = math.inf		,							SCREAMING_SNAKE_CASE = -math.inf		,							SCREAMING_SNAKE_CASE = math.inf		,							SCREAMING_SNAKE_CASE = -math.inf		,							SCREAMING_SNAKE_CASE = False		,							SCREAMING_SNAKE_CASE = 1_00		,							SCREAMING_SNAKE_CASE = 0.01		,							SCREAMING_SNAKE_CASE = 1		,							):


		"""simple docstring"""




		lowercase__				= False
		lowercase__				= search_prob
		lowercase__				= start_temperate
		lowercase__				= []
		lowercase__				= 0
		lowercase__				= None

		while not search_end:
				lowercase__				= current_state.score()
				if best_state is None or current_score > best_state.score():
						lowercase__				= current_state
				scores.append(SCREAMING_SNAKE_CASE	)
				iterations += 1
				lowercase__				= None
				lowercase__				= current_state.get_neighbors()
				while (
				    next_state is None and neighbors
				):  # till we do not find a neighbor that we can move to
						lowercase__				= random.randint(0		,							len(SCREAMING_SNAKE_CASE	) - 1	)  # picking a random neighbor
						lowercase__				= neighbors.pop(SCREAMING_SNAKE_CASE	)
						lowercase__				= picked_neighbor.score() - current_score

						if (
						    picked_neighbor.x > max_x
						    or picked_neighbor.x < min_x
						    or picked_neighbor.y > max_y
						    or picked_neighbor.y < min_y
						):
								continue  # neighbor outside our bounds

						if not find_max:
								lowercase__				= change * -1  # in case we are finding minimum
						if change > 0:  # improves the solution
								lowercase__				= picked_neighbor
						else:
								lowercase__				= (math.e) ** (
								    change / current_temp
								)  # probability generation function
								if random.random() < probability:  # random number within probability
										lowercase__				= picked_neighbor
				lowercase__				= current_temp - (current_temp * rate_of_decrease)

				if current_temp < threshold_temp or next_state is None:
						# temperature below threshold, or could not find a suitable neighbor
						lowercase__				= True
				else:
						lowercase__				= next_state

		if visualization:
				from matplotlib import pyplot as plt

				plt.plot(range(SCREAMING_SNAKE_CASE	)		,							SCREAMING_SNAKE_CASE	)
				plt.xlabel('''Iterations'''	)
				plt.ylabel('''Function values'''	)
				plt.show()
		return best_state


if __name__ == "__main__":
			def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


					"""simple docstring"""




					return (x**2) + (y**2)

			# starting the problem with initial coordinates (12, 47)
			lowerCAmelCase			=				SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			lowerCAmelCase			=				simulated_annealing(
			    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
			)
			print(
			    'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
			)

			# starting the problem with initial coordinates (12, 47)
			lowerCAmelCase			=				SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			lowerCAmelCase			=				simulated_annealing(
			    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
			)
			print(
			    'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
			)
			def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


					"""simple docstring"""




					return (3 * x**2) - (6 * y)

			lowerCAmelCase			=				SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			lowerCAmelCase			=				simulated_annealing(prob, find_max=False, visualization=True)
			print(
			    'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
			    f"""{local_min.score()}"""
			)

			lowerCAmelCase			=				SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			lowerCAmelCase			=				simulated_annealing(prob, find_max=True, visualization=True)
			print(
			    'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
			    f"""{local_min.score()}"""
			)



"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
